
import pandas as pd 
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook
from matplotlib.patches import Rectangle
import seaborn as sns
import pydicom as dcm
import os
import pydicom
import glob
%matplotlib inline


PATH="./rsna-pneumonia-detection-challenge"
print('Files:',os.listdir(PATH))

Files: ['stage_2_sample_submission.csv', 'stage_2_train_images', 'GCP Credits Request Link - RSNA.txt', 'models', 'stage_2_detailed_class_info.csv', 'stage_2_train_labels.csv', 'stage_2_test_images']


os.listdir(PATH+'/stage_2_train_images')[0:5]

['1fa9f43b-3f5f-4c16-8731-c88d71ee6e0b.dcm',
 'd4b8eb6a-ebf1-4752-b5a6-4737c5890061.dcm',
 '7437e61c-7aba-478a-9398-0eed93ba42f5.dcm',
 'b165b941-4ec6-4f1b-9f03-2e908de24266.dcm',
 'fb7fb7fe-5887-4ae3-93c8-33615b96d11c.dcm']


os.path.abspath(PATH)

'/home/yihsin/Documents/Pneumonia/rsna-pneumonia-detection-challenge'


os.path.dirname(PATH+'/stage_2_train_images')

'./rsna-pneumonia-detection-challenge'


for item in fileList:
    filename = os.path.basename(item[0])
    copyfile(item[0], os.path.join("/Users/username/Desktop/testPhotos", filename))


# load data
class_info_df = pd.read_csv(PATH+'/stage_2_detailed_class_info.csv')
train_labels_df = pd.read_csv(PATH+'/stage_2_train_labels.csv')
sample_submission_df = pd.read_csv(PATH+'/stage_2_sample_submission.csv')


# image class information
print(f"row: {class_info_df.shape[0]}, column: {class_info_df.shape[1]}")
class_info_df.head()

row: 30227, column: 2


class_info_dropdu_df=class_info_df.drop_duplicates('patientId', keep='first')
class_info_dropdu_df.shape

(26684, 2)


# image bounding box information
print(f"row: {train_labels_df.shape[0]}, column: {train_labels_df.shape[1]}")
train_labels_df.head()

row: 30227, column: 6


# predict format
print(f"row: {sample_submission_df.shape[0]}, column: {sample_submission_df.shape[1]}")
sample_submission_df.head()

row: 3000, column: 2


train_class_df = train_labels_df.merge(class_info_df, left_on='patientId', right_on='patientId', how='inner')


print(f"row: {train_class_df.shape[0]}, column: {train_class_df.shape[1]}, patient numbers:{len(train_class_df.patientId.unique())}")
train_class_df.head()

row: 37629, column: 7, patient numbers:26684


# get lists of all train/test dicom filepaths
train_dcm_fps = glob.glob(PATH+'/stage_2_train_images/*.dcm')
#test_dcm_fps = glob.glob(PATH+'/stage_2_test_images/*.dcm')

# read each file into a list (using stop_before_pixels to avoid reading the image for speed and memory savings)
train_dcms = [pydicom.read_file(x, stop_before_pixels=True) for x in train_dcm_fps]
#test_dcms = [pydicom.read_file(x, stop_before_pixels=True) for x in test_dcm_fps]


def parse_dcm_metadata(dcm):
    unpacked_data = {}
    group_elem_to_keywords = {}
    # iterating here to force conversion from lazy RawDataElement to DataElement
    for d in dcm:
        pass
    # keys are pydicom.tag.BaseTag, values are pydicom.dataelem.DataElement
    for tag, elem in dcm.items():
        tag_group = tag.group
        tag_elem = tag.elem
        keyword = elem.keyword
        group_elem_to_keywords[(tag_group, tag_elem)] = keyword
        value = elem.value
        unpacked_data[keyword] = value
    return unpacked_data, group_elem_to_keywords

train_meta_dicts, tag_to_keyword_train = zip(*[parse_dcm_metadata(x) for x in train_dcms])
#test_meta_dicts, tag_to_keyword_test = zip(*[parse_dcm_metadata(x) for x in test_dcms])


# join all the dicts
unified_tag_to_key_train = {k:v for dict_ in tag_to_keyword_train for k,v in dict_.items()}
#unified_tag_to_key_test = {k:v for dict_ in tag_to_keyword_test for k,v in dict_.items()}


train_df = pd.DataFrame.from_records(data=train_meta_dicts)
'''
test_df = pd.DataFrame.from_records(data=test_meta_dicts)
train_df['dataset'] = 'train'
test_df['dataset'] = 'test'
df = pd.concat([train_df, test_df])
'''

"\ntest_df = pd.DataFrame.from_records(data=test_meta_dicts)\ntrain_df['dataset'] = 'train'\ntest_df['dataset'] = 'test'\ndf = pd.concat([train_df, test_df])\n"


train_df.head()


# separating PixelSpacing list to single values
train_df['PixelSpacing_x'] = train_df['PixelSpacing'].apply(lambda x: x[0])
train_df['PixelSpacing_y'] = train_df['PixelSpacing'].apply(lambda x: x[1])
train_df = train_df.drop(['PixelSpacing'], axis='columns')

# x and y are always the same
assert sum(train_df['PixelSpacing_x'] != train_df['PixelSpacing_y']) == 0


# ReferringPhysicianName appears to just be empty strings
assert sum(train_df['ReferringPhysicianName'] != '') == 0

# SeriesDescription appears to be 'view: {}'.format(ViewPosition)
set(train_df['SeriesDescription'].unique())

{'view: AP', 'view: PA'}


nunique_all=train_df.aggregate('nunique')
nunique_all

SpecificCharacterSet               1
SOPClassUID                        1
SOPInstanceUID                 26684
StudyDate                          1
StudyTime                          1
AccessionNumber                    1
Modality                           1
ConversionType                     1
ReferringPhysicianName             1
SeriesDescription                  2
PatientName                    26684
PatientID                      26684
PatientBirthDate                   1
PatientSex                         2
PatientAge                        97
BodyPartExamined                   1
ViewPosition                       2
StudyInstanceUID               26684
SeriesInstanceUID              26684
StudyID                            1
SeriesNumber                       1
InstanceNumber                     1
PatientOrientation                 1
SamplesPerPixel                    1
PhotometricInterpretation          1
Rows                               1
Columns                            1
BitsAllocated                      1
BitsStored                         1
HighBit                            1
PixelRepresentation                1
LossyImageCompression              1
LossyImageCompressionMethod        1
PixelSpacing_x                    18
PixelSpacing_y                    18
dtype: int64


# drop constant cols and other two from above
train_df = train_df.drop(nunique_all[nunique_all == 1].index.tolist() + ['ReferringPhysicianName', 'SeriesDescription'], axis='columns')

# now that we have a clean metadata dataframe we can merge back to our initial tabular data with target and class info
train_df = train_df.merge(train_class_df, how='left', left_on='PatientID', right_on='patientId')

train_df['PatientAge'] = train_df['PatientAge'].astype(int)


print(train_df.shape)
train_df.head()

(37629, 17)


# df now has multiple rows for some patients (those with multiple bounding boxes in label_w_class)
# so creating one with no duplicates for patients
train_df_deduped = train_df.drop_duplicates('PatientID', keep='first')


fig, axes = plt.subplots(1, 2, figsize=(14, 7))
p = sns.countplot(x='ViewPosition', hue='class', data=train_df, ax=axes[0], palette="coolwarm")
_ = p.set_title('With multiple')
p = sns.countplot(x='ViewPosition', hue='class', data=train_df_deduped, ax=axes[1], palette="coolwarm")
_ = p.set_title('Single row per patient')


fig, axes = plt.subplots(1, 2, figsize=(14, 7))

p = sns.distplot(train_df[train_df['ViewPosition']=='AP']['PatientAge'], hist=True, kde=False, color='red', label='AP', ax=axes[0])
p = sns.distplot(train_df[train_df['ViewPosition']=='PA']['PatientAge'], hist=True, kde=False, color='gray', label='PA', ax=axes[0])
_ = p.set_ylabel('Count')
_ = p.legend()
_ = p.set_title('With multiple')

p = sns.distplot(train_df_deduped[train_df_deduped['ViewPosition']=='AP']['PatientAge'], hist=True, kde=False, color='red', label='AP', ax=axes[1])
p = sns.distplot(train_df_deduped[train_df_deduped['ViewPosition']=='PA']['PatientAge'], hist=True, kde=False, color='gray', label='PA', ax=axes[1])
_ = p.set_ylabel('Count')
_ = p.legend()
_ = p.set_title('Single row per patient')


fig, axes = plt.subplots(1, 2, figsize=(14, 7))

p = sns.countplot(train_df['PixelSpacing_x'], hue='ViewPosition', data=train_df, ax=axes[0], palette="coolwarm")
_ = p.set_xticklabels([x for x in p.get_xticklabels()],rotation=90)
_ = p.set_title('With multiple')

p = sns.countplot(train_df_deduped['PixelSpacing_x'], hue='ViewPosition', data=train_df_deduped, ax=axes[1], palette="coolwarm")
_ = p.set_xticklabels([x for x in p.get_xticklabels()],rotation=90)
_ = p.set_title('Single row per patient')


fig, axes = plt.subplots(3, 2, figsize=(14, 9), sharex=True)
for i, _class in enumerate(train_df['class'].dropna().unique()):
    p = sns.distplot(train_df[train_df['class']==_class]['PatientAge'], hist=True, kde=False, ax=axes[i, 0])
    _ = p.set_ylabel('Count')
    _ = p.set_xlabel(f'PatientAge - {_class}')
    if i == 0: p.set_title('With multiple')
    
    p = sns.distplot(train_df_deduped[train_df_deduped['class']==_class]['PatientAge'], hist=True, kde=False, ax=axes[i, 1])
    _ = p.set_ylabel('Count')
    _ = p.set_xlabel(f'PatientAge - {_class}')
    if i == 0: p.set_title('Single row per patient')


f, ax = plt.subplots(1,1, figsize=(6,4))
total = float(len(class_info_df))
sns.countplot(class_info_df['class'],order =['Lung Opacity','Normal','No Lung Opacity / Not Normal'], palette='coolwarm')
#sns.countplot(class_info_df['class'],order =class_info_df['class'].value_counts().index,palette='coolwarm')
for p in ax.patches:
    height = p.get_height()
    ax.text(p.get_x()+p.get_width()/2.,
            height-1/20*height,
            '{:1.0f} '.format(height) +'({:1.1f}%)'.format(100*height/total),
            ha="center") 
plt.xticks(rotation=15)
plt.show()


f, ax = plt.subplots(1,1, figsize=(6,4))
total = float(len(class_info_dropdu_df))
sns.countplot(class_info_dropdu_df['class'],order =['Lung Opacity','Normal','No Lung Opacity / Not Normal'], palette='coolwarm')
#sns.countplot(class_info_df['class'],order =class_info_df['class'].value_counts().index,palette='coolwarm')
for p in ax.patches:
    height = p.get_height()
    ax.text(p.get_x()+p.get_width()/2.,
            height-1/20*height,
            '{:1.0f} '.format(height) +'({:1.1f}%)'.format(100*height/total),
            ha="center") 
plt.xticks(rotation=15)
plt.show()


train_class_df.groupby('class')['Target'].value_counts()

class                         Target
Lung Opacity                  1         16957
No Lung Opacity / Not Normal  0         11821
Normal                        0          8851
Name: Target, dtype: int64


fig, ax = plt.subplots(nrows=1,figsize=(12,6))
tmp = train_class_df.groupby('Target')['class'].value_counts()
df = pd.DataFrame(data={'Exams': tmp.values}, index=tmp.index).reset_index()
sns.barplot(ax=ax,x = 'Target', y='Exams',hue='class',data=df, palette="Paired", edgecolor=".2")
plt.title("Chest exams class and Target")
for p in ax.patches:
    height = p.get_height()
    ax.text(p.get_x()+p.get_width()/2.,
            height+200,
            '{:1.0f} '.format(height) +'({:1.1f}%)'.format(100*height/total),
            ha="center") 
plt.show()

posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values


train_PATH=PATH+'/stage_2_train_images/'
train_PATH

'./rsna-pneumonia-detection-challenge/stage_2_train_images/'


len(os.listdir(train_PATH))

26684


dcm_file = train_PATH+'3ebe379f-90fc-44a7-9602-71c5abfdd50a.dcm'
dcm_data = pydicom.read_file(dcm_file)
im = dcm_data.pixel_array


plt.imshow(im, cmap='bone')

<matplotlib.image.AxesImage at 0x7f41dc3e0c10>


# Forked from `https://www.kaggle.com/peterchang77/exploratory-data-analysis`
def parse_data(df):
    """
    Method to read a CSV file (Pandas dataframe) and parse the 
    data into the following nested dictionary:

      parsed = {
        
        'patientId-00': {
            'dicom': path/to/dicom/file,
            'label': either 0 or 1 for normal or pnuemonia, 
            'boxes': list of box(es)
        },
        'patientId-01': {
            'dicom': path/to/dicom/file,
            'label': either 0 or 1 for normal or pnuemonia, 
            'boxes': list of box(es)
        }, ...

      }

    """
    # --- Define lambda to extract coords in list [y, x, height, width]
    extract_box = lambda row: [row['y'], row['x'], row['height'], row['width']]

    parsed = {}
    for n, row in df.iterrows():
        # --- Initialize patient entry into parsed 
        pid = row['patientId']
        if pid not in parsed:
            parsed[pid] = {
                'dicom': './rsna-pneumonia-detection-challenge/stage_2_train_images/%s.dcm' % pid,
                'label': row['Target'],
                'boxes': []}

        # --- Add box if opacity is present
        if parsed[pid]['label'] == 1:
            parsed[pid]['boxes'].append(extract_box(row))

    return parsed

parsed = parse_data(train_labels_df)

def draw(data):
    """
    Method to draw single patient with bounding box(es) if present 

    """
    # --- Open DICOM file
    d = pydicom.read_file(data['dicom'])
    im = d.pixel_array

    # --- Convert from single-channel grayscale to 3-channel RGB
    im = np.stack([im] * 3, axis=2)

    # --- Add boxes with random color if present
    for box in data['boxes']:
        #rgb = np.floor(np.random.rand(3) * 256).astype('int')
        rgb = [255, 251, 204] # Just use yellow
        im = overlay_box(im=im, box=box, rgb=rgb, stroke=5)

    plt.imshow(im, cmap=plt.cm.gist_gray)
    plt.axis('off')

def overlay_box(im, box, rgb, stroke=5):
    """
    Method to overlay single box on image

    """
    # --- Convert coordinates to integers
    box = [int(b) for b in box]
    
    # --- Extract coordinates
    y1, x1, height, width = box
    y2 = y1 + height
    x2 = x1 + width

    im[y1:y1 + stroke, x1:x2] = rgb
    im[y2:y2 + stroke, x1:x2] = rgb
    im[y1:y2, x1:x1 + stroke] = rgb
    im[y1:y2, x2:x2 + stroke] = rgb

    return im


draw(parsed['00436515-870c-4b36-a041-de91049b9ab4'])


def show_dicom_images_with_boxes(data):
    img_data = list(data.T.to_dict().values())
    f, ax = plt.subplots(3,3, figsize=(16,18))
    for i,data_row in enumerate(img_data):
        patientImage = data_row['patientId']+'.dcm'
        imagePath = os.path.join(PATH,"stage_2_train_images/",patientImage)
        data_row_img_data = dcm.read_file(imagePath)
        modality = data_row_img_data.Modality
        view = data_row_img_data.ViewPosition
        age = data_row_img_data.PatientAge
        sex = data_row_img_data.PatientSex
        data_row_img = dcm.dcmread(imagePath)
        ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) 
        ax[i//3, i%3].axis('off')
        ax[i//3, i%3].set_title('ID: {}\nModality: {} view: {}\n Age: {} Sex: {} Target: {}\nClass: {}'.format(
                data_row['patientId'],modality, view, age, sex, data_row['Target'], data_row['class']))
        rows = train_class_df[train_class_df['patientId']==data_row['patientId']]
        box_data = list(rows.T.to_dict().values())
        for j, row in enumerate(box_data):
            ax[i//3, i%3].add_patch(Rectangle(xy=(row['x'], row['y']),
                        width=row['width'],height=row['height'], 
                        color="yellow",alpha = 0.1))   
    plt.show()


show_dicom_images_with_boxes(train_class_df[train_class_df['Target']==1].sample(9))


import matplotlib.patches as patches
def show_dicom_images_with_boxes_v2(data):
    img_data = list(data.T.to_dict().values())
    f, ax = plt.subplots(3,3, figsize=(16,18))
    for i,data_row in enumerate(img_data):
        patientImage = data_row['patientId']+'.dcm'
        imagePath = os.path.join(PATH,"stage_2_train_images/",patientImage)
        data_row_img_data = dcm.read_file(imagePath)
        modality = data_row_img_data.Modality
        view = data_row_img_data.ViewPosition
        age = data_row_img_data.PatientAge
        sex = data_row_img_data.PatientSex
        im = data_row_img_data.pixel_array
        ax[i//3, i%3].imshow(im, cmap=plt.cm.bone) 
        ax[i//3, i%3].axis('off')
        ax[i//3, i%3].set_title('ID: {}\nModality: {} view: {}\n Age: {} Sex: {} \nClass: {}'.format(
                data_row['patientId'],modality, view, age, sex, data_row['class']))
        rows = train_class_df[train_class_df['patientId']==data_row['patientId']]
        box_data = list(rows.T.to_dict().values())
        for j, row in enumerate(box_data):
            ax[i//3, i%3].add_patch(Rectangle(xy=(row['x'], row['y']),
                        width=row['width'],height=row['height'], 
                        edgecolor="yellow",facecolor='none'))   
    plt.show()


show_dicom_images_with_boxes_v2(train_class_df[train_class_df['Target']==1].sample(9))


show_dicom_images_with_boxes_v2(train_class_df[train_class_df['class']=='Normal'].sample(9))


show_dicom_images_with_boxes_v2(train_class_df[train_class_df['class']=='No Lung Opacity / Not Normal'].sample(9))

	patientId	x	y	width	height	Target
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1

	patientId	PredictionString
0	0000a175-0e68-4ca4-b1af-167204a7e0bc	0.5 0 0 100 100
1	0005d3cc-3c3f-40b9-93c3-46231c3eb813	0.5 0 0 100 100
2	000686d7-f4fc-448d-97a0-44fa9c5d3aa6	0.5 0 0 100 100
3	000e3a7d-c0ca-4349-bb26-5af2d8993c3d	0.5 0 0 100 100
4	00100a24-854d-423d-a092-edcf6179e061	0.5 0 0 100 100

	patientId	x	y	width	height	Target	class
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0	Normal
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1	Lung Opacity

	SpecificCharacterSet	SOPClassUID	SOPInstanceUID	StudyDate	Modality	ConversionType	SeriesDescription	...	PhotometricInterpretation	Rows	Columns	PixelSpacing	BitsAllocated	BitsStored	HighBit	LossyImageCompression	LossyImageCompressionMethod
0	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.10083.15178743...	19010101	CR	WSD	view: AP	...	MONOCHROME2	1024	1024	[0.139, 0.139]	8	8	7	01	ISO_10918_1
1	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.3000.151787429...	19010101	CR	WSD	view: PA	...	MONOCHROME2	1024	1024	[0.14300000000000002, 0.14300000000000002]	8	8	7	01	ISO_10918_1
2	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.16999.15178744...	19010101	CR	WSD	view: AP	...	MONOCHROME2	1024	1024	[0.168, 0.168]	8	8	7	01	ISO_10918_1
3	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.21147.15178744...	19010101	CR	WSD	view: AP	...	MONOCHROME2	1024	1024	[0.168, 0.168]	8	8	7	01	ISO_10918_1
4	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.3537.151787430...	19010101	CR	WSD	view: AP	...	MONOCHROME2	1024	1024	[0.168, 0.168]	8	8	7	01	ISO_10918_1

	SOPInstanceUID	PatientName	PatientID	PatientSex	PatientAge	ViewPosition	StudyInstanceUID	SeriesInstanceUID	PixelSpacing_x	PixelSpacing_y	patientId	x	y	width	height	Target	class
0	1.2.276.0.7230010.3.1.4.8323329.10083.15178743...	1fa9f43b-3f5f-4c16-8731-c88d71ee6e0b	1fa9f43b-3f5f-4c16-8731-c88d71ee6e0b	F	74	AP	1.2.276.0.7230010.3.1.2.8323329.10083.15178743...	1.2.276.0.7230010.3.1.3.8323329.10083.15178743...	0.139	0.139	1fa9f43b-3f5f-4c16-8731-c88d71ee6e0b	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
1	1.2.276.0.7230010.3.1.4.8323329.3000.151787429...	d4b8eb6a-ebf1-4752-b5a6-4737c5890061	d4b8eb6a-ebf1-4752-b5a6-4737c5890061	F	52	PA	1.2.276.0.7230010.3.1.2.8323329.3000.151787429...	1.2.276.0.7230010.3.1.3.8323329.3000.151787429...	0.143	0.143	d4b8eb6a-ebf1-4752-b5a6-4737c5890061	NaN	NaN	NaN	NaN	0	Normal
2	1.2.276.0.7230010.3.1.4.8323329.16999.15178744...	7437e61c-7aba-478a-9398-0eed93ba42f5	7437e61c-7aba-478a-9398-0eed93ba42f5	M	53	AP	1.2.276.0.7230010.3.1.2.8323329.16999.15178744...	1.2.276.0.7230010.3.1.3.8323329.16999.15178744...	0.168	0.168	7437e61c-7aba-478a-9398-0eed93ba42f5	NaN	NaN	NaN	NaN	0	Normal
3	1.2.276.0.7230010.3.1.4.8323329.21147.15178744...	b165b941-4ec6-4f1b-9f03-2e908de24266	b165b941-4ec6-4f1b-9f03-2e908de24266	M	69	AP	1.2.276.0.7230010.3.1.2.8323329.21147.15178744...	1.2.276.0.7230010.3.1.3.8323329.21147.15178744...	0.168	0.168	b165b941-4ec6-4f1b-9f03-2e908de24266	354.0	375.0	164.0	293.0	1	Lung Opacity
4	1.2.276.0.7230010.3.1.4.8323329.21147.15178744...	b165b941-4ec6-4f1b-9f03-2e908de24266	b165b941-4ec6-4f1b-9f03-2e908de24266	M	69	AP	1.2.276.0.7230010.3.1.2.8323329.21147.15178744...	1.2.276.0.7230010.3.1.3.8323329.21147.15178744...	0.168	0.168	b165b941-4ec6-4f1b-9f03-2e908de24266	354.0	375.0	164.0	293.0	1	Lung Opacity

RSNA Pnumonia Exploratory Data Analysis(EDA)¶

Data preparation¶

load data¶

data description¶

data format¶

merge dataframe¶

Check missing data¶

Meta data preparation¶

Exploratory data¶

Exploratory meta data¶

Exploratory tabular data¶

Display image data¶